This article uses the narratives of survivors of honor killing to show that women's agency is the reason for life threats because it undermines masculine domination. The findings show that life ...threats are made against women engaging in behaviors not aligned to cultural norms as perceived by male members of their family, to escape shame and gossip, and it is a manifestation of men losing control over women. These survivors of honor-based violence have undermined masculine domination by acting in unanticipated ways and by fleeing to a shelter home in the face of overwhelming cultural sanctions and structural inequalities.
PurposeThe purpose of this paper is to explore the features of health misinformation on social media sites (SMSs). The primary goal of the study is to investigate the salient features of health ...misinformation and to develop a tool of features to help users and social media companies identify health misinformation.Design/methodology/approachEmpirical data include 1,168 pieces of health information that were collected from WeChat, a dominant SMS in China, and the obtained data were analyzed through a process of open coding, axial coding and selective coding. Then chi-square test and analysis of variance (ANOVA) were adopted to identify salient features of health misinformation.FindingsThe findings show that the features of health misinformation on SMSs involve surface features, semantic features and source features, and there are significant differences in the features of health misinformation between different topics. In addition, the list of features was developed to identify health misinformation on SMSs.Practical implicationsThis study raises awareness of the key features of health misinformation on SMSs. It develops a list of features to help users distinguish health misinformation as well as help social media companies filter health misinformation.Originality/valueTheoretically, this study contributes to the academic discourse on health misinformation on SMSs by exploring the features of health misinformation. Methodologically, the paper serves to enrich the literature around health misinformation and SMSs that have hitherto mostly drawn data from health websites.
This paper studies distributed state estimation for a dynamic system based on measurements collected by every node in a sensor network. Depending on the communications among nodes, information ...spreads across the network node by node through iterations. Then, nodes work collaboratively to estimate the system state timely. Most existing networked filters aim to make all nodes reach consensus at the centralized estimation with sufficient communications. To accommodate limited communication resources in practice, this paper proposes to optimize the entire network's estimation accuracy given a fixed number of communications. This is done by optimizing the sequence of node activations for selective communication. We propose two selective activation schemes: link activation (LA) and star activation (SA). In each iteration, they activate a single link and a single star (i.e., a node with all its neighbors), respectively. We develop two iterative distributed filters (DF): LA based (LA-DF) and SA based (SA-DF). LA-DF and SA-DF possess many important properties. For example, they are unbiased, convergent, stable, and credible. Finally, we analyze the performance of our filters and provide simulation results compared with existing filters to verify the superiority of the proposed filters.
Epidemic gossip has proven a reliable and efficient technique for sharing information in a distributed network. Much of this reliability and efficiency derives from processes collaborating, sharing ...the work of distributing information. As a result of this collaboration, processes may receive information that was not originally intended for them. For example, some process may act as an intermediary, aggregating and forwarding messages from some set of sources to some set of destinations. But what if rumors are
confidential
? In that case, only processes that were originally intended to receive the rumor should be allowed to learn the rumor. This blatantly contradicts the basic premise of epidemic gossip, which assumes that processes can collaborate. In fact, if only processes in a rumor’s “destination set” participate in gossiping that rumor, we show that high message complexity is unavoidable. A natural approach is to rely on cryptography, for example, assuming that each process has a well-known public-key that can be used to encrypt the rumor. In a dynamic system, with changing sets of destinations, such a process seems potentially expensive. In this paper, we propose a scheme in which each rumor is broken into multiple fragments using a very simple coding scheme; any given fragment provides no information about the rumor, while together, the fragments can be reassembled into the original rumor. The processes collaborate in disseminating the rumor fragments in such a way that no process outside of a rumor’s destination set ever receives all the fragments of a rumor, while every process in the destination set eventually learns all the fragments. Notably, our solution operates in an environment where rumors are dynamically and continuously injected into the system and processes are subject to crashes and restarts. In addition, the presented scheme can tolerate a moderate amount of collusions among
curious
processes without a substantial increase in cost; curious processes are non-malicious processes that are not in a rumor’s destination set, and still want to learn the rumor (that is, collect all fragments of the rumor).
Purpose Gossip spreads like wildfire, damaging relationships, decaying trust and creating a negative work environment. This study aims to investigate the relationship between negative workplace ...gossip (NWG) and quiet quitting (QQ), while considering the mediating effects of workplace stress and emotional exhaustion (EE). Design/methodology/approach Drawing upon the conservation of resource theory, the study aimed to comprehend this association in the context of 267 employees from diverse sectors in India, including health care, IT, banking and education. Through a three-wave time lagged survey design, using partial least squares structural equation modeling, significant findings were uncovered. Findings The results revealed a positive link between NWG and QQ. There was also a positive correlation between NWG and workplace stress. In addition, workplace stress and EE were found to mediate the relationship between NWG and QQ. Practical implications The findings have implications for both theory and practice. Organizations should consider implementing strategies to mitigate the prevalence of negative gossip and foster a healthier work environment, promoting employee well-being and retention. Originality/value The study reveals the “black box” between NWG and QQ, adding to the body of knowledge on the novel concept of QQ. Second, the study expands the literature on NWG, by examining impact path of how it leads to stress and EE, leading to QQ.
Currently, social media is full of rumors. To stop rumors from spreading further, rumor detection has received increasing attention. Recent rumor detection methods treat all propagation paths and all ...nodes on the paths as equally important, resulting in models that fail to extract the key features. In addition, most methods ignore user features, leading to limitations in the performance improvement of rumor detection. To address these problems, we propose a Dual-Attention Network model on propagation Tree structures named DAN-Tree, where a node-and-path dual-attention mechanism is designed to organically fuse deep structure and semantic information on the propagation structures of rumors, and path oversampling and structural embedding are employed to enhance the learning of deep structures. Finally, we deeply integrate user profiles into the propagation trees in DAN-Tree, thus proposing the DAN-Tree++ model to further improve performance. Empirical studies on four rumor datasets have shown that DAN-Tree outperforms the state-of-the-art rumor detection models learning on propagation structures, and the results on two datasets with user information validate the superior performance of DAN-Tree++ over other models using both user profiles and propagation structures. What’s more, DAN-Tree, especially DAN-Tree++, has achieved the best performance on early detection tasks.
Discrete time Markov models are extremely popular for analyzing a categorical time series due to their wide applicability. Especially, higher-order Markov models can capture more complex dependence ...of a categorical time series. However, with increasing order, the complexity of the model also increases in terms of number of parameters. In this dissertation, we consider a more general parsimonious modeling approach is given by Sparse Markov Models (SMMs).In Chapter 2, we give a thorough review of the large sample properties of Markov chains, which is useful in extending the large sample results in chapter 3 for higher order Markov models including SMM. In Chapter 4, we develop an elegant method of fitting SMMs based on convex clustering algorithms, which minimizes a convex and penalized loss function. Theoretical results establish model selection consistency of our method for large sample size. Extensive simulation and real data example in classifying RNA sequences of different viruses demonstrate the wide applicability of such method. In Chapter 5, we extend the previous method for a more general class of divergence measure. We provide theoretical results which enable us to find a range of the regularization parameter for which the true underlying clusters can be identified. A more relaxed method of convex clustering is proposed, namely SR2C2, which performs comparably with the traditional methods, but in much less time. To compare the time complexity and the model performances for different algorithms, extensive simulation studies have been conducted. In Chapter 6, we propose a bootstrap based prediction algorithm for predicting the h-step ahead future, demonstrated with extensive simulation studies. A computationally efficient method of constructing simultaneous 100(1 − α)% prediction sets for the future observations is introduced, based on an anomaly scoring method. We demonstrate this method in detecting anomalous genes in Helicobacter Pylori bacteria.
